Analysis of Deep-Learning Methods in an ISO/TS 15066-Compliant Human-Robot Safety Framework

TL;DR

This paper presents a deep-learning-based safety framework for human-robot collaboration that dynamically adjusts robot speeds based on human proximity, improving efficiency while maintaining safety limits.

Contribution

It introduces a novel safety framework utilizing deep learning for human body part recognition, enabling optimized robot operation within ISO/TS 15066 standards.

Findings

Up to 15% reduction in cycle time compared to traditional safety systems

Effective differentiation of human body parts for safety and efficiency

Validation across four deep learning approaches for human body extraction

Abstract

Over the last years collaborative robots have gained great success in manufacturing applications where human and robot work together in close proximity. However, current ISO/TS-15066-compliant implementations often limit the efficiency of collaborative tasks due to conservative speed restrictions. For this reason, this paper introduces a deep-learning-based human-robot-safety framework (HRSF) that aims at a dynamical adaptation of robot velocities depending on the separation distance between human and robot while respecting maximum biomechanical force and pressure limits. The applicability of the framework was investigated for four different deep learning approaches that can be used for human body extraction: human body recognition, human body segmentation, human pose estimation, and human body part segmentation. Unlike conventional industrial safety systems, the proposed HRSF differentiates individual human body parts from other objects, enabling optimized robot process execution. Experiments demonstrated a quantitative reduction in cycle time of up to 15% compared to conventional safety technology.